Automatic learning of optimal prevention strategies for latent infectious diseases

Project Details

Description

We will design a framework that allows large scale simulations to be carried out in the context of the transmission of latent infectious diseases. This software will be designed to meet the computational challenges of these large-scale simulations and that it can easily be extended to other pathogens. We will also be able to simulate multiple pathogens at the same time to study the possible interaction between these pathogens. The source code of the software will be freely available so that other research institutions can build on our solutions.
AcronymFWOSB9
StatusFinished
Effective start/end date1/01/1631/12/19

Keywords

  • latent infectious diseases

Flemish discipline codes

  • Numerical analysis
  • Deep Reinforcement Learning for Large-Scale Epidemic Control

    Libin, P., Moonens, A., Verstraeten, T., Perez Sanjines, F. R., Hens, N., Lemey, P. & Nowe, A., 1 Jan 2021, Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V. Dong, Y., Ifrim, G., Mladenić, D., Saunders, C. & Van Hoecke, S. (eds.). Ghent, Belgium: Springer, Vol. 5. p. 155-170 16 p. (Lecture Notes in Computer Science; vol. 12461).

    Research output: Chapter in Book/Report/Conference proceedingConference paper

    Open Access
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